Current state-of-the-art resource management systems leverage Machine Learning (ML) methods to enable the efficient use of heterogeneous memory hardware, deployed across emerging computing platforms. While machine intelligence can be effectively used to learn and predict complex data access patterns of modern analytics, the use of ML over the exploded data sizes and memory footprints is prohibitive for its practical system-level integration. For this reason, recent solutions use existing lightweight historical information to predict the access behavior of majority of the application pages, and train ML models over a small page subset. To maximize application performance improvements, the pages selected for machine learning-based management are identified with elaborate page selection methods. These methods involve the calculation of detailed performance estimates depending on the configuration of the hybrid memory platform. This paper aims to reduce such vast operational overheads, that further exacerbate the existing high overheads of using machine intelligence, in return for high performance and efficiency. To this end, we build Cronus, an image-based pipeline for selecting pages for ML-based management. We visualize memory access patterns and reveal spatial and temporal correlations among the selected pages, that current methods fail to leverage. We then use the created images to detect patterns and select page groups for machine learning model deployment. Cronus drastically reduces the operational costs, while preserving the effectiveness of the page selection and achieved performance of machine intelligent hybrid memory management. This work makes a case that visualization and computer vision methods can unlock new insights and reduce the operational complexity of emerging systems solutions.
more »
« less
Kleio: A Hybrid Memory Page Scheduler with Machine Intelligence
The increasing demand of big data analytics for more main memory capacity in datacenters and exascale computing environments is driving the integration of heterogeneous memory technologies. The new technologies exhibit vastly greater differences in access latencies, bandwidth and capacity compared to the traditional NUMA systems. Leveraging this heterogeneity while also delivering application performance enhancements requires intelligent data placement. We present Kleio, a page scheduler with machine intelligence for applications that execute across hybrid memory components. Kleio is a hybrid page scheduler that combines existing, lightweight, history-based data tiering methods for hybrid memory, with novel intelligent placement decisions based on deep neural networks. We contribute new understanding toward the scope of benefits that can be achieved by using intelligent page scheduling in comparison to existing history-based approaches, and towards the choice of the deep learning algorithms and their parameters that are effective for this problem space. Kleio incorporates a new method for prioritizing pages that leads to highest performance boost, while limiting the resulting system resource overheads. Our performance evaluation indicates that Kleio reduces on average 80% of the performance gap between the existing solutions and an oracle with knowledge of future access pattern. Kleio provides hybrid memory systems with fast and effective neural network training and prediction accuracy levels, which bring significant application performance improvements with limited resource overheads, so as to lay the grounds for its practical integration in future systems.
more »
« less
- Award ID(s):
- 1822972
- NSF-PAR ID:
- 10104915
- Date Published:
- Journal Name:
- HPDC '19 Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing
- Page Range / eLocation ID:
- 37 to 48
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Emerging workloads benefit from massive memory capacities provided by hybrid memory platforms. Recent system-level hybrid memory management solutions integrate machine learning methods to better predict complex data access behaviors. Given the substantial associated learning overheads, such solutions train parallel recurrent neural networks to learn the access patterns at the granularity of a page for a carefully selected page subset. Our observation reveals that the size of this subset varies immensely across workload classes, sizes and patterns. Increasing the granularity at the level of a page group will help reduce the aggregate learning overheads. Yet, unsupervised machine learning clustering methods are not practical to use in this context. Instead, this paper builds Coeus - a page grouping mechanism for machine learning-based hybrid memory management. Coeus is simple, robust and efficient. Coeus leverages data reuse insights to fine-tune the granularity at which patterns are interpreted by the system. As a result, Coeus creates large clusters of pages that share the same access behavior, in a practical way. Coeus reduces by almost 3x the associated learning overheads. In addition, Coeus achieves 3x higher application performance, by the combined effects of applying machine learning to more pages and by performing management operations at better granularity, compared to configurations of existing hybrid memory managers.more » « less
-
Current state-of-the-art systems for hybrid memory management are enriched with machine intelligence. To enable the practical use of Machine Learning (ML), system-level page schedulers focus the ML model training over a small subset of the applications’ memory footprint. At the same time, they use existing lightweight historical information to predict the access behavior of majority of the pages. To maximize application performance improvements, the pages selected for machine learning-based management are identified with elaborate page selection methods. These methods involve the calculation of detailed performance estimates depending on the configuration of the hybrid memory platform. This paper explores the opportunities to reduce such operational overheads of machine learning-based hybrid memory page schedulers via use of visualization techniques to depict memory access patterns, and reveal spatial and temporal correlations among the selected pages, that current methods fail to leverage. We propose an initial version of a visualization pipeline for prioritizing pages for machine learning, that is independent of the hybrid memory configuration. Our approach selects pages whose ML-based management delivers, on average, performance levels within 5% of current solutions, while reducing by 75 Ă— the page selection time. We discuss future directions and make a case that visualization and computer vision methods can unlock new insights and reduce the operational complexity of emerging systems solutions.more » « less
-
null (Ed.)Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D-stacked memories or charge-sharing-based bitwise operations in dynamic random-access memory (DRAM). However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management.more » « less
-
Locality-based migration strategies are widely used in existing memory space management. Such type of strategies are consistently confronts with challenges in efficiently managing pages migration within constrained memory space, especially when new architecture such as hybrid of DRAM and NVM are emerging. Here we propose TransMigrator, an innovative predictive page migration model based on transformer architecture, which obtains a qualitative leap in the breadth and accuracy of prediction compared with traditional local-based methods. TransMigrator utilizes an end-to-end neural network to learn memory access behavior and page migration record in the long-term history and predict the most likely next page to fetch. Furthermore, a migration-management mechanism is designed to support the page-feeding from predictor, which in another way enhance the model robustness. The model achieves an average prediction accuracy better than 0.72, and saves an average of 0.24 access time overhead compared to strategies such as AC-CLOCK, THMigrator, and VC-HMM.more » « less